A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011

Improvement of resistance to rice blast and bacterial leaf streak by CRISPR/Cas9-mediated mutagenesis of Pi21 and OsSULTR3;6 in rice (Oryza sativa L.)

Rice (Oryza sativa L.) is a staple food in many countries around the world, particularly in China. The production of rice is seriously affected by the bacterial leaf streak and rice blast, which can reduce rice yield or even cause it to fail to be harvested. In this study, susceptible material 58B was edited by CRISPR/Cas9, targeting a target of the Pi21 gene and a target of the effector-binding element (EBE) of the OsSULTR3;6 gene, and the mutants 58b were obtained by Agrobacterium-mediated method. The editing efficiency of the two targets in the T0 generation was higher than 90.09%, the homozygous mutants were successfully selected in the T0 generation, and the homozygous mutation rate of each target was higher than 26.67%. The expression of the edited pi21 and EBE of Ossultr3;6 was significantly reduced, and the expression of defense responsive genes was significantly upregulated after infected with rice blast. The lesion areas of rice blast and bacterial leaf streak were significantly reduced in 58b, and the resistance of both was effectively improved. Furthermore, the gene editing events did not affect the agronomic traits of rice. In this study, the resistance of 58b to rice blast and bacterial leaf streak was improved simultaneously. This study provides a reference for using Clustered Regularly Interspaced Short Palindromic Repeats/Cas9 (CRISPR/Cas9) to accelerate the improvement of rice varieties and the development of new materials for rice breeding

Integration of Solexa sequences on an ultradense genetic map in Brassica rapa L.

<p>Abstract</p> <p>Background</p> <p>Sequence related amplified polymorphism (SRAP) is commonly used to construct high density genetic maps, map genes and QTL of important agronomic traits in crops and perform genetic diversity analysis without knowing sequence information. To combine next generation sequencing technology with SRAP, Illumina's Solexa sequencing was used to sequence tagged SRAP PCR products.</p> <p>Results</p> <p>Three sets of SRAP primers and three sets of tagging primers were used in 77,568 SRAP PCR reactions and the same number of tagging PCR reactions respectively to produce a pooled sample for Illumina's Solexa sequencing. After sequencing, 1.28 GB of sequence with over 13 million paired-end sequences was obtained and used to match Solexa sequences with their corresponding SRAP markers and to integrate Solexa sequences on an ultradense genetic map. The ultradense genetic bin map with 465 bins was constructed using a recombinant inbred (RI) line mapping population in <it>B. rapa</it>. For this ultradense genetic bin map, 9,177 SRAP markers, 1,737 integrated unique Solexa paired-end sequences and 46 SSR markers representing 10,960 independent genetic loci were assembled and 141 unique Solexa paired-end sequences were matched with their corresponding SRAP markers. The genetic map in <it>B. rapa </it>was aligned with the previous ultradense genetic map in <it>B. napus </it>through common SRAP markers in these two species. Additionally, SSR markers were used to perform alignment of the current genetic map with other five genetic maps in <it>B. rapa </it>and <it>B. napus</it>.</p> <p>Conclusion</p> <p>We used SRAP to construct an ultradense genetic map with 10,960 independent genetic loci in <it>B. rapa </it>that is the most saturated genetic map ever constructed in this species. Using next generation sequencing, we integrated 1,878 Solexa sequences on the genetic map. These integrated sequences will be used to assemble the scaffolds in the <it>B. rapa </it>genome. Additionally, this genetic map may be used for gene cloning and marker development in <it>B. rapa </it>and <it>B. napus</it>.</p

Sufficient Conditions for Global Convergence of Differential Evolution Algorithm

The differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter optimization algorithms. The theoretical studies on DE have gradually attracted the attention of more and more researchers. However, few theoretical researches have been done to deal with the convergence conditions for DE. In this paper, a sufficient condition and a corollary for the convergence of DE to the global optima are derived by using the infinite product. A DE algorithm framework satisfying the convergence conditions is then established. It is also proved that the two common mutation operators satisfy the algorithm framework. Numerical experiments are conducted on two parts. One aims to visualize the process that five convergent DE based on the classical DE algorithms escape from a local optimal set on two low dimensional functions. The other tests the performance of a modified DE algorithm inspired of the convergent algorithm framework on the benchmarks of the CEC2005

The Application of a Three-Dimensional Deterministic Model in the Study of Debris Flow Prediction Based on the Rainfall-Unstable Soil Coupling Mechanism

As debris flow is one of the most destructive natural disasters in many parts of the world, the assessment and management of future debris flows with proper forecasting methods are crucial for the safety of life and property. So increasing attention has been paid to the forecasting methods on debris flows. A debris flow forecasting method based on the rainfall-unstable soil coupling mechanism (R-USCM) is presented in the current study. This method is based on the debris flow formation mechanism. The density of sediment is introduced as an evaluation index to determine the susceptibility of debris flow occurrence. The forecasting method includes two phases: (1) rainfall and soil coupling and (2) runoff and unstable soil coupling. Scoops3D, a three-dimensional (3D) model for analyzing slope stability, was introduced into the debris flow forecasting method. In order to test the forecasting accuracy of this method, Jiaohe County was selected as a research area, and the serious debris flow disasters attributed to strong rainfall on 20 July 2017 were taken as the research case. By comparing the forecasting results with the debris flow distribution map for Jiaohe County, the method based on the R-USCM is feasible for forecasting debris flows at the regional scale. The application of the Scoops3D model can more reasonably analyze the slope stability than the traditional two dimensional (2D) method and improve the forecasting ability of debris flows

Genetic diversity of Shaanxi soybean landraces based on agronomic traits and SSR markers

Publisher's version/PDFGenetic diversity of a primary core collection of 91 soybean landraces from Shaanxi Province, China, was analyzed using simple sequence repeat (SSR) markers and agronomic traits. A total of 250 alleles were detected in the 91 soybean accessions, with a mean of 7.14 alleles per locus. The mean of polymorphism information content (PIC) was 0.26, ranged from 0.11 for Satt184 to 0.60 for Satt242. UPGMA cluster analysis and PCA analysis clearly showed that, 91 accessions formed two major clusters which generally correspond to geographic origin. Cluster I contained 76 soybean landraces and it was further separated into five subgroups (I-1 to I-5). Cluster II (northern group) included 15 accessions from northern Shaanxi. Group I-1 (Guanzhong group) contained 19 landraces, with 16 from Guanzhong, 3 from northern Shaanxi. Group I-2 (southern group I) composed of 13 accessions from southern Shaanxi and 2 from Guanzhong. Group I-3 (mixture group) contained 18 landraces, with 10 landraces from Guanzhong and 8 from southern Shaanxi. Group I-4 (southern group II) contained 21 accessions, of which 20 from southern Shaanxi and 1 from northern Shaanxi. Group I-5 (southern group III) included only 2 southern Shaanxi landraces. AMOVA analysis showed that, a significant proportion of variance (94.28%) was due to variation within populations

Subspace Clustering Mutation Operator for Developing Convergent Differential Evolution Algorithm

Many researches have identified that differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter algorithms for global optimization problems. However, a stagnation problem still exists in DE variants. In order to overcome the disadvantage, two improvement ideas have gradually appeared recently. One is to combine multiple mutation operators for balancing the exploration and exploitation ability. The other is to develop convergent DE variants in theory for decreasing the occurrence probability of the stagnation. Given that, this paper proposes a subspace clustering mutation operator, called SC_qrtop. Five DE variants, which hold global convergence in probability, are then developed by combining the proposed operator and five mutation operators of DE, respectively. The SC_qrtop randomly selects an elite individual as a perturbation’s center and employs the difference between two randomly generated boundary individuals as a perturbation’s step. Theoretical analyses and numerical simulations demonstrate that SC_qrtop prefers to search in the orthogonal subspace centering on the elite individual. Experimental results on CEC2005 benchmark functions indicate that all five convergent DE variants with SC_qrtop mutation outperform the corresponding DE algorithms